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Model Selection

Given a set of candidate models, the goal of Model Selection is to select the model that best approximates the observed data and captures its underlying regularities. Model Selection criteria are defined such that they strike a balance between the goodness of fit, and the generalizability or complexity of the models.

Source: Kernel-based Information Criterion

Papers

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Showing 1441–1450 of 2050 papers

TitleStatusHype
Physics-Based Learning for Robotic Environmental Sensing—0
Model Complexity of Deep Learning: A Survey—0
Model Consistency of Partly Smooth Regularizers—0
Model family selection for classification using Neural Decision Trees—0
Model-free Estimation of Latent Structure via Multiscale Nonparametric Maximum Likelihood—0
Modeling cumulative biological phenomena with Suppes-Bayes Causal Networks—0
Modeling flexible behavior with remapping-based hippocampal sequence learning—0
Modeling methodology for the accurate and prompt prediction of symptomatic events in chronic diseases—0
Modeling non-stationarities in high-frequency financial time series—0
Modeling Sheep pox Disease from the 1994-1998 Epidemic in Evros Prefecture, Greece—0
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